Method and apparatus for monitoring website access data

ABSTRACT

A method for monitoring and analyzing website visit data includes acquiring real-time source data of sessions established between a client terminal and a server; classifying the real-time source data of sessions into a plurality of categories based on the website and a session identifier; caching the categories of the real-time source data of sessions in the memory; if a categorized session is valid, calculating visit effect data of the session using the source data; consolidating the visit effect data of the session with a sum of visit effect data; and updating the sum of visit effect data; if a categorized session is invalid, calculating failure effect data of the session; consolidating the failure effect data of the session with the sum of visit effect data; and deleting the source data of the session from the memory.

RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2014/084489, entitled “METHOD AND APPARATUS FOR MONITORINGWEBSITE ACCESS DATA” filed on Aug. 15, 2014, which claims priority toChinese Patent Application No. 201310380434.4, entitled “Method andApparatus for Monitoring Website Access Data” filed on Aug. 28, 2013,both of which are incorporated by reference in their entirety.

TECHNICAL FIELD

The disclosed implementations relate to the field of Internet dataprocessing technology, and in particular, to a method and an apparatusfor monitoring and analyzing website visit data.

BACKGROUND

Technologies to monitor websites visit data are adopted to analyze andfurther, optimize the internet performance. Such technologies includedata monitoring, data collection, data analysis, and data reporting,etc. The operation efficiency and visit traffic of a website can beimproved using the tracked and analyzed data, and the functional goalsthat a website developer expects can be achieved.

The current technology collects statistic data of a website thatincludes numbers of page views (PV) and numbers of unique visitors (UV).

PV is a major criterion to measure a website, a news link of a website,and traffic of a website. Monitoring the varying trend of the website PVand analyzing the reasons for the varying trend is regular work for manywebsite administrators. The word “page” in the term “page views”generally refers to an ordinary html page, but may also refer to htmlcontents dynamically-generated by php, jsp, etc. An html content requestfrom a browser may be considered to be a PV, which is accumulated into asum of PV.

UV refers to human beings that access and browse a webpage via internet.For example, user A opens the homepage of a certain website, andregisters as a member on a computer. A moment later, user B registers asanother member using the same computer. As user A and user B use thesame computer with the same IP address. An official counter of thewebsite records login information from a single IP address. However, afurther monitoring system may determine the number of actual usersaccording to other conditions. Further, a website developer can getaccurate and complete information of the users of the website. Forinstance, using the information of registered users, different computerssharing an IP address in an internet café or a computer room can bedistinguished.

The current technologies utilize a big data platform to monitor andanalyze websites. Visit data of websites are collected on a daily basis,and daily visit effect data including PVs and UVs are calculated.However, the big data platform requires centralized computation of thedaily collected data, and thus, requires high performance computers.Further, processing a large amount of data daily is inefficient. In viewof the foregoing, it is difficult for the current technologies toprovide real-time monitoring and analysis of the website visit data fromvarious perspective views, for instance, real-time PVs and UVscalculated based on visit traffic and visit source, etc.

SUMMARY

In accordance with some implementations of the present application, amethod for monitoring and analyzing website visit data comprises: at acomputer device having one or more processors and memory storingprograms for execution by the one or more processors, acquiringreal-time source data of sessions established between a client terminaland a server; classifying the real-time source data into a plurality ofcategories based on the website and a session identifier; caching thecategorized real-time source data in the memory; if a sessioncorresponding to the categorized real-time source data is valid,calculating visit effect data of the session using the source data;consolidating the visit effect data of the session with a sum of visiteffect data; and updating the sum of visit effect data; if a sessioncorresponding to the categorized real-time source data is invalid,calculating failure effect data of the session; consolidating thefailure effect data of the session with the sum of visit effect data;and deleting the source data of the session from the memory.

In some embodiments, the method for monitoring and analyzing websitevisit data further comprises sorting the real-time source data ofsessions in accordance with an occurrence time of the session.

In some embodiments, the method for monitoring and analyzing websitevisit data further comprises acquiring a user identifier for eachsession; acquiring user portrait data that corresponds to the useridentifier; and supplementing the user portrait data into the sourcedata of the session.

In some embodiments, the method for monitoring and analyzing websitevisit data further comprises coding a uniform resource locator (URL) inthe source data of the session in accordance with a pre-determinedcompression coding format; and replacing the URL in the source data ofthe session with the coded URL.

In some embodiments, the method for monitoring and analyzing websitevisit data further comprises determining whether the source data of thesession includes a webpage visiting record; and if the source data ofthe session does not include a webpage visiting record, furtherdetermining a frequency of a pre-defined operation, and if the frequencyof the pre-defined operation exceeds a pre-set threshold, deleting datathat associates with the pre-defined operation from the source data ofthe session.

In some embodiments, the visit effect data of the session includes atleast one of four dimensional data: traffic analysis data, sourceanalysis data, visitor analysis data, and visitor behavior analysisdata.

In some embodiments, each dimensional data includes at least page viewsdata and unique visitors data.

In some embodiments, the method for monitoring and analyzing websitevisit data further comprises calculating a unique visitors number foreach dimensional data, wherein if an update speed is higher than apre-defined speed, the unique visitors number is calculated using a setstructure, and the user identifier of the session with respect to thedimensional data is stored in a designated memory module; and if theupdate speed is lower than the pre-defined speed, the unique visitorsnumber is calculated through determining whether a uvset structure of acurrent day includes a user identifier.

In accordance with some implementations of the present application, asystem for monitoring and analyzing website visit data comprises: one ormore processors; memory; and one or more programs stored in the memoryand configured for execution by the one or more processors, the one ormore programs including instructions for: acquiring real-time sourcedata of sessions established between a client terminal and a server;classifying the real-time source data into a plurality of categoriesbased on the website and a session identifier; caching the categorizedreal-time source data in the memory; if a session corresponding to thecategorized real-time source data is valid, calculating visit effectdata of the session using the source data; consolidating the visiteffect data of the session with a sum of visit effect data; and updatingthe sum of visit effect data; if a session corresponding to thecategorized real-time source data is invalid, calculating failure effectdata of the session; consolidating the failure effect data of thesession with the sum of visit effect data; and deleting the source dataof the session from the memory.

In accordance with some implementations of the present application, anon-transitory computer readable storage medium, storing one or moreprograms for execution by one or more processors of a service processingsystem including instructions for: acquiring real-time source data ofsessions established between a client terminal and a server; classifyingthe real-time source data into a plurality of categories based on thewebsite and a session identifier; caching the categorized real-timesource data of sessions in the memory; if a session corresponding to thecategorized real-time source data is valid, calculating visit effectdata of the session using the source data; consolidating the visiteffect data of the session with a sum of visit effect data; and updatingthe sum of visit effect data; if a session corresponding to thecategorized real-time source data is invalid, calculating failure effectdata of the session; consolidating the failure effect data of thesession with the sum of visit effect data; and deleting the source dataof the session from the memory.

BRIEF DESCRIPTION OF DRAWINGS

The aforementioned implementation of the present application as well asadditional implementations will be more clearly understood as a resultof the following detailed description of the various aspects of thepresent application when taken in conjunction with the drawings. Likereference numerals refer to corresponding parts throughout the severalviews of the drawings.

FIG. 1 is a flow chart of a method for monitoring website access data ofthe present application;

FIG. 2 is a first subflow chart of a specific embodiment of a method formonitoring website access data;

FIG. 3 is a second subflow chart of a specific embodiment of a methodfor monitoring website access data;

FIG. 4 is a third subflow chart of a specific embodiment of a method formonitoring website access data; and

FIG. 5 is a structure block diagram of an apparatus for monitoringwebsite access data of the present application.

DETAILED DESCRIPTION

The present application will be described in further detail below withreference to drawings and specific embodiments.

FIG. 1 is a flow chart of a method for monitoring the website accessdata of the present application.

At step 101, real-time source data of sessions established between aclient terminal and a server is acquired.

Source data of a session is raw data that records a user conversation,which is a series of request-response established between a clientterminal and a server. The server may recognize a client terminal fromthe received request. When the server receives a first request from anunknown client terminal, a conversation is established. Further, whenthe server receives an instruction to terminate the conversation, or theserver does not receive any requests from the client terminal for apre-set time period, the conversation is deemed terminated.

The first request sent by the client to the server of the website maynot be the first interaction between the client and the server. Thefirst request refers to a request for establishing a session. It isgenerally referred to as a “first” request because it is the beginningof counting the number of requests (logically), and also the beginningthat the server recognizes the client. For example, when the user logsin or adds a merchandize to a shopping cart, a session is initiated andestablished.

The source data of a session may be divided into source access data andsource behavior data, which are obtained from an access data source anda behavior data source, respectively. The source session data mayinclude website information and session identification information. Thesource access data may include, a user identification (ID), an IPaddress, cookie information, starting and ending time of a session,uniform resource locators (URLs) for accessing the page, and datacarried in commonly-used Internet protocols such as hypertext transferprotocol (HTTP).

The source behavior data may also include a user ID, an IP address,cookie information, starting and ending time of a session, URLs foraccessing the page and the like. The source behavior data may furtherinclude the sequence information that records the user's action andbehavior on the webpage, for example, user logging in, registering,voting, sending message in Weibo, etc.

The data source from which session data can be retrieved is usually theserver of a website. The server of the website may monitor theconversations between users and the website, and generate correspondingsession data. As the session data is generated and updated in real time,the present application can acquire the real-time source data ofsessions that includes newly-generated session data, and updated sessiondata.

At step 102, the real-time source data is classified into a plurality ofcategories based on the website and a session identifier.

In some embodiments, to achieve a higher read-write speed and improvereal-time performance, the storage device refers to a local memory of acomputer. The memory is used as an illustrative example of the storagedevice in the embodiments discussed below; however, the storage devicemay also refer to an external memory device.

Step 102 is a process of data construction. The data is stored in astructure with a basic unit of storage being a session, i.e., each unitof the stored data indicates a single session. According to the presentembodiment, the source session data is classified into a plurality ofcategories based on the website and a session identifier, and furthercached in the memory.

If n websites are monitored and each website generates m sessions, thenn*m basic data units can be constructed, and each data unit stores thedata of one corresponding session. The amount of source data associatedwith a monitored website is typically huge. With the data constructionof the present application, the huge amount of data source is slicedinto pieces of information at a session level, which can facilitatereal-time computation and updating.

At step 103, the categorized real-time source data of sessions arecached in the memory.

At step 104, the method determines whether a session corresponding tothe categorized real-time source data is valid.

If a session corresponding to the categorized real-time source data isvalid, at step 105, visit effect data of the session is calculated usingthe source data; and further at step 106, the visit effect data of thesession is consolidated with a sum of visit effect data.

If a session corresponding to the categorized real-time source data isinvalid, failure effect data of the session is calculated at step 107;the failure effect data of the session is consolidated with the sum ofvisit effect data at step 108; and the source data of the session isdeleted from the memory at step 109.

At step 110, the sum of visit effect data is updated.

According to the present embodiment, newly acquired session data isstored in the memory as classified, and an updating computation of thevisit effect data is triggered based on the newly acquired session data.Each updating computation is substantially an increment calculation, andgenerates incremental visit effect data.

As the visit effect data is computed and updated based on each sessiondata according to the present application, the computation efficiency isimproved. The present application may further analyze the visit effectdata in accordance with difference aspects of the received session data.

In some embodiments, the aspects of the visit effect data may include atleast one of four dimensional data types: traffic analysis data, sourceanalysis data, visitor analysis data, and visitor behavior analysisdata.

In some embodiments, each aspect of the visit effect data may include atleast page views (PV) data and unique visitors (UV) data. For instance,traffic analysis data may include a total PV number and UV number of awebsite and/or a webpage associated with the website.

The source analysis data may include information of the client terminalfrom which a request is sent, such as operation system, browser type,desktop, or mobile terminal, etc., and the PV number and UV number thatassociate with the above-noted information.

The visitor analysis data may include type of visitors that access awebsite and/or a webpage, such as, sex, age, location, hobbies of thevisit, and the PV number and UV number that associate with theabove-noted types. For example, the visitor analysis data may includethe PV number and UV number associated with a male visitor who visitsthe website A, and the PV number and UV number associated with a visitorlocated in Beijing who visits the website A.

The visitor behavior analysis data may include types of user behaviorswhen a user visits a website and/or a webpage of the website, forexample, for Weibo website, whether the user participates in thediscussion and voting, watches a video, clicks a web advertisement, andthe PV number and UV number that associate with the above-noted types.For example, the visitor behavior analysis data may include the PVnumber and UV number associated with a click of a web advertisementposted on the webpage C of the website B.

The computation methods of the PV number and UV number adopt existingtechnologies, and therefore, are not discussed herein.

In some embodiments, a session ending time is compared with a lastupdating time of the visit effect data. If the last updating time islater than the session ending time, the session is determined asinvalid, and a failure effect data is calculated.

In some embodiments, calculating failure effect data further includes:calculating a total time length of the session, extracting webpagevisits information, generating webpage visiting path and associationinformation, and calculating an exit rate of the session. In someembodiments, the exit rate of the session is defined as a ratio ofexiting frequencies from a website after visiting only one webpageversus total visits of the website.

In some embodiments, session data is deleted from the memory after thefailure effect data is calculated. Accordingly, memory is refreshed andthe system resources can be effectively used in computation andanalysis.

According to the present application, the source data of sessions isclassified based on a session unit instead of a day unit. As the sessionduration is in general much shorter than a day, and the session data mayinclude complete conversation between a client and the server, thepresent application can utilize the system resources more efficientlyand can further provide website analysis based on more aspects of thesession data.

According to the present application, the real-time performance ofupdating the visit data can be greatly improved, and the updatingaccuracy can achieve at the minute level. Further, the presentapplication does not depend on large data platforms and encompassesmulti-dimensional real-time effect computations, for example, trafficanalysis, source analysis, visitor analysis, and behavior analysis, etc.Because the present application adopts incremental update computation,the computation amount for each updating is relatively small, and theresource is released immediately after the computation, therebyimproving the system resource utilization. In addition, the requirementon system computation ability is much less than the traditional bigplatforms. Accordingly, although the computation dimensionalitiesincrease, real-time performance of multi-dimensional computation can beimproved with high efficiency.

In some embodiments, the real-time source data of sessions is sorted inaccordance with session occurring time. For example, source session dataa, source session data b and source session data c are acquired insequence. Based on the session time recorded in the source session data,source session data c occurs first followed by source session data b andsource session data a. After sorting based on the session time, thesequence reads as source session data c, source session data b andsource session data a.

In some embodiments, after acquiring source session data of the websitein real time and before classifying and caching the source session datato the memory based on the site and session identification, i.e., whenconducting data construction, the present application may furthercomprise acquiring the user identification (such as the user ID, or theuser's IP address) of the source session data, acquiring the userportrait data that corresponds to the user identification, andsupplementing the user portrait data to the source session data.

The user portrait data may indicate different attributes of the user,for example, sex, location, hobbies, etc. A user portrait data sourcerefers to a specialized database that stores the user's portrait datathrough statistical analysis based on the historical data. According tothe present application, the user portrait data is supplemented to thesource session data, which is then used to calculate visit effect data.According to the present application, the calculated visit effect datacan be more accurate and comprehensive.

In another embodiment, after acquiring source session data of thewebsite in real time and before classifying and caching the sourcesession data to the memory based on the site and session identification,i.e., when conducting data construction, the present application mayfurther comprise encoding URLs in the source session data according to aspecified compression encoding format and replacing corresponding URLsin the source session data with encoded URLs. The reason for theabove-noted steps is that the URL has a large number of characters whichoccupy more storage resource, and the number of characters of theencoded URL is relatively small, thereby saving the storage resource. Inparticular, as the session data is cached to the memory with limitedstorage, the encoding of the URL may greatly save the memory resourceand improve the process efficiency.

In yet another embodiment, after acquiring source session data of thewebsite in real time and before classifying and caching the sourcesession data to the memory based on the site and session identification,i.e., when conducting data construction, the present application mayfurther comprise determining whether the source data of the sessionincludes a webpage visiting record; and if the source data of thesession does not include a webpage visiting record, further determininga frequency of a pre-defined operation, if the frequency of thepre-defined operation exceeds a pre-set threshold, deleting data thatassociates with the pre-defined operation from the source data of thesession.

In some embodiments, the method further comprises calculating a UVnumber for each dimensional data. If an update speed is higher than apre-defined speed (i.e., data for traffic analysis, visitor analysis,and visitor behavior analysis is updated in minutes), the UV number iscalculated using a set structure, and the user identifier of the sessionwith respect to the dimensional data is stored in a designated memorymodule. If the update speed is lower than the pre-defined speed (i.e.,source analysis data is normally updated in a day or a week), the UVnumber is calculated through determining whether a uvset structure of acurrent day includes a user identifier.

In some embodiments, the visit effect data may be exported to aninternal memory in real-time, and exported to an external memory in aperiod of time, including association and path information to update thedata structure. The visit effect data exported to the external memorymay be further used to generate data report forms. The updatingfrequency of the external memory may be adjusted based on theperformance of the memory.

FIG. 2 is a first subflow chart of a specific embodiment of a method formonitoring the website access data. FIG. 3 is a second subflow chart ofa specific embodiment of a method for monitoring website access data.FIG. 4 is a third subflow chart of a specific embodiment of a method formonitoring website access data. In some embodiments, the three subflowsmay be performed in parallel.

The first subflow illustrated in FIG. 2 is a processing flow of thesource session data comprises the steps discussed below.

At step 201, source session data is acquired and scanned.

At step 202, whether the acquired source session data needs to besynchronized is determined according to the session time in the sourcesession data; and if the acquired source session data needs to besynchronized, synchronization will be conducted on the data, i.e., timesynchronization adjustment is conducted on the acquired source sessiondata.

At step 203, the source session data is adjusted and stored. This mayinclude encoding the URLs in the source session data according to aspecified compression encoding format, replacing the URLs in the sourcesession data with encoded URLs, and classifying and caching the sourcesession data to the memory according to the site and sessionidentification in accordance with the time sequence.

At step 204, a determination is made as to whether the currently scannedsession data is the first page of a corresponding session.

At step 205, corresponding user portrait data according to the useridentification is acquired, and the user portrait data is added to thesource session data.

In general, a session corresponds to user portrait data. In thisembodiment, the user portrait data is acquired at the first page of thesession, and no user portrait data needs to be acquired at subsequentpages of the session.

At step 206, the session data is updated using the obtained userportrait data of the session.

At step 207, incremental access data of each dimension is calculated,for example, the traffic analysis data, the source analysis data, thevisitor analysis data, and the visitor behavior analysis data.

At step 208, the calculated incremental access data at step 207 ismerged into the previous total access data to obtain the latest totalaccess data.

The second subflow illustrated in FIG. 3 comprises the steps discussedbelow.

At step 301, the system is in a sleep state after regularly clearingcalculation results from the memory.

At step 302, a current time is acquired.

At step 303, a determination is made as to whether the current timereaches a preset time of data clearing calculation. If so, at step 304,each session of each site being monitored is traversed. If not, thesystem returns to the sleep state.

At step 305, a determination is made as to whether the current sessionfails. If not, the system jumps to step 307. If so, at step 306, sessionfailure data is calculated and merged into the latest total access data.At step 307, the session data of the failed session is cleared from thememory, and the memory resources are released.

The third subflow illustrated in FIG. 4 comprises the steps discussedbelow.

At step 401, the system is in a sleep state after regularly updatingresults taken-in and output. At step 402, the current time is acquired.At step 403, a determination is made as to whether the current timereaches a preset time of taken-in and output.

If so, at step 404, each site being monitored is traversed. If not, thenthe system returns to the sleep state.

At step 405, the latest total access data of the site is exported to aspecified database.

FIG. 5 is a structure block diagram of an apparatus for monitoringwebsite visit data. The apparatus includes a data acquisition module 501for acquiring the source session data of the website in real time. Adata construction module 502 classifies and caches the source sessiondata to the memory based on the site and session identification. A datacalculation module 503 computes new visit effect data using the acquiredsession data, and consolidates the new visit effect data into a sum ofvisit effect data to update the sum of the visit effect data. A dataclearing module 504 determines whether the classified and cached sessionis valid. If the session is invalid, the data clearing module 504calculates the failure effect data, consolidates the failure effect datainto the sum of visit effect data, and clears the session data of thefailed session from the memory. A system state module 505 collects thesystem operation parameters and an output module 506 exports the visiteffect data to an external memory.

In some embodiments, the data acquisition module 501 may furthercomprise a synchronization submodule 511 for conducting timesynchronization adjustment to all source session data based on thesession time. Further, the data construction module 502 may be used forclassifying and caching the source session data to the memory based onthe website and session identifiers after the time synchronizationadjustment.

In another embodiment, the data construction module 502 may be used foracquiring a user identifier in source session data before classifyingand caching the source session data to the memory, acquiring userportrait data that associates with the user identifier, andsupplementing the user portrait data to the source session data.

In yet another embodiment, the data construction module 502 may be usedfor encoding uniform resource locators (URLs) in the source session dataaccording to a specified compression encoding format, and replacing theURLs in the source session data with the encoded URLs.

In yet another embodiment, the data construction module 502 may be usedfor determining whether the source data of the session includes awebpage visiting record. If the source data of the session does notinclude a webpage visiting record, the data construction module 502further determines a frequency of a pre-defined operation and, if thefrequency of the pre-defined operation exceeds a pre-set threshold,delete data that associates with the pre-defined operation from thesource data of the session.

In some embodiments, the data calculation module 503 may be used tocalculate a UV number for each dimensional data. If an update speed ishigher than a pre-defined speed, the UV number is calculated using a setstructure. In addition, the user identifier of the session with respectto the dimensional data is stored in a designated memory module. If theupdate speed is lower than the pre-defined speed, the UV number iscalculated through determining whether a uvset structure of a currentday includes a user identifier.

In some embodiments, the system state module 506 may further maintainthe system operation parameters and output a visible view of theinternal system. The system state may include the number of sessions inuse, the number of sessions established in a past time period, thenumber of sessions being release, system clocks taken for processing therequests, and the number of requests being processed, etc.

While particular embodiments are described above, it will be understoodit is not intended to limit the present application to these particularembodiments. On the contrary, the present application includesalternatives, modifications and equivalents that are within the spiritand scope of the appended claims. Numerous specific details are setforth in order to provide a thorough understanding of the subject matterpresented herein. But it will be apparent to one of ordinary skill inthe art that the subject matter may be practiced without these specificdetails. In other instances, well-known methods, procedures, components,and circuits have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Although the terms first, second, etc. may be used herein to describevarious elements, these elements should not be limited by these terms.These terms are only used to distinguish one element from another. Forexample, first ranking criteria could be termed second ranking criteria,and, similarly, second ranking criteria could be termed first rankingcriteria, without departing from the scope of the present application.First ranking criteria and second ranking criteria are both rankingcriteria, but they are not the same ranking criteria.

The terminology used in the description of the present applicationherein is for the purpose of describing particular embodiments only andis not intended to be limiting of the present application. As used inthe description of the present application and the appended claims, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, operations, elements,components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

Although some of the various drawings illustrate a number of logicalstages in a particular order, stages that are not order dependent may bereordered and other stages may be combined or broken out. While somereordering or other groupings are specifically mentioned, others will beobvious to those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit the present application to the precise forms disclosed. Manymodifications and variations are possible in view of the aboveteachings. The implementations were chosen and described in order tobest explain principles of the present application and its practicalapplications, to thereby enable others skilled in the art to bestutilize the present application and various implementations with variousmodifications as are suited to the particular use contemplated.Implementations include alternatives, modifications and equivalents thatare within the spirit and scope of the appended claims. Numerousspecific details are set forth in order to provide a thoroughunderstanding of the subject matter presented herein. But it will beapparent to one of ordinary skill in the art that the subject matter maybe practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theimplementations.

What is claimed is:
 1. A method for monitoring and analyzing websitevisit data comprising: at a computer device having one or moreprocessors and memory storing programs for execution by the one or moreprocessors, acquiring real-time source data of sessions establishedbetween a client terminal and a server, comprising: scanning theacquired real-time source data of a respective session; determiningwhether the scanned source data is a first page of the respectivesession; in accordance with a determination that the scanned source datais the first page of the respective session, acquiring user portraitdata corresponding to a user identifier associated with the real-timesource data of the respective session; and updating the real-time sourcedata of the respective session using the user portrait data; classifyingthe real-time source data into a plurality of categories based on thewebsite and a session identifier; caching the categorized real-timesource data of a session corresponding to the session identifier in thememory, the session having a session ending time; and performing thefollowing operations in real-time: comparing the session ending timewith a last updating time of a sum of visit effect data associated withthe website; if the session ending time is later than the last updatingtime, calculating visit effect data of the session using the categorizedreal-time source data of the session, further including generatingtraffic analysis data, source analysis data, visitor analysis data, andvisitor behavior analysis data associated with the website;consolidating the visit effect data of the session with the sum of visiteffect data associated with the website; keeping the categorizedreal-time source data of the session in the memory to be combined withsubsequent real-time source data of the session before the session iscompleted; and if the session ending time is no later than the lastupdating time, calculating completed visit effect data of the sessionusing a completed source data of the session stored in the memory,further including generating a total length of the session, webpagevisits information, webpage visiting path and association information,and an exit rate of the session associated with the website;consolidating the completed visit effect data of the session with thesum of visit effect data associated with the website; and deleting thecompleted source data of the session from the memory to refresh thememory; updating the sum of visit effect data associated with thewebsite.
 2. The method for monitoring and analyzing website visit dataof claim 1, further comprising: sorting the real-time source data inaccordance with an occurrence time of the session.
 3. The method formonitoring and analyzing website visit data of claim 1, furthercomprising: acquiring the user identifier for each session; andsupplementing the user portrait data into the source data of thesession.
 4. The method for monitoring and analyzing website visit dataof claim 1, further comprising: coding a uniform resource locator (URL)in the source data of the session in accordance with a pre-determinedcompression coding format; and replacing the URL in the source data ofthe session with the coded URL.
 5. The method for monitoring andanalyzing website visit data of claim 1, wherein each dimensional dataincludes at least page views data and unique visitors data.
 6. Themethod for monitoring and analyzing website visit data of claim 5,further comprising calculating a unique visitors number for eachdimensional data, wherein if an update speed is higher than apre-defined speed, the unique visitors number is calculated using a setstructure, and the user identifier of the session with respect to thedimensional data is stored in a designated memory module; and if theupdate speed is lower than the pre-defined speed, the unique visitorsnumber is calculated through determining whether a set structure ofunique visitors of a current day includes a user identifier.
 7. A systemfor monitoring and analyzing website visit data comprising: one or moreprocessors; memory; and one or more programs stored in the memory andconfigured for execution by the one or more processors, the one or moreprograms including instructions for: acquiring real-time source data ofsessions established between a client terminal and a server, comprising:scanning the acquired real-time source data of a respective session;determining whether the scanned source data is a first page of therespective session; in accordance with a determination that the scannedsource data is the first page of the respective session, acquiring userportrait data corresponding to a user identifier associated with thereal-time source data of the respective session; and updating thereal-time source data of the respective session using the user portraitdata; classifying the real-time source data into a plurality ofcategories based on the website and a session identifier; caching thecategorized real-time source data of a session corresponding to thesession identifier in the memory, the session having a session endingtime; and performing the following operations in real-time: comparingthe session ending time with a last updating time of a sum of visiteffect data associated with the website; if the session ending time islater than the last updating time, calculating visit effect data of thesession using the categorized real-time source data of the session,further including generating traffic analysis data, source analysisdata, visitor analysis data, and visitor behavior analysis dataassociated with the website; consolidating the visit effect data of thesession with the sum of visit effect data associated with the website;keeping the categorized real-time source data of the session in thememory to be combined with subsequent real-time source data of thesession before the session is completed; and if the session ending timeis no later than the last updating time, calculating completed visiteffect data of the session using a completed source data of the sessionstored in the memory, further including generating a total length of thesession, webpage visits information, webpage visiting path andassociation information, and an exit rate of the session associated withthe website; consolidating the completed visit effect data of thesession with the sum of visit effect data associated with the website;and deleting the completed source data of the session from the memory torefresh the memory; updating the sum of visit effect data associatedwith the website.
 8. The system for monitoring and analyzing websitevisit data of claim 7, wherein the one or more programs further includeinstructions for: sorting the real-time source data in accordance withan occurrence time of the session.
 9. The system for monitoring andanalyzing website visit data of claim 7, wherein the one or moreprograms further include instructions for: acquiring the user identifierfor each session; and supplementing the user portrait data into thesource data of the session.
 10. The system for monitoring and analyzingwebsite visit data of claim 7, wherein the one or more programs furtherinclude instructions for: coding a uniform resource locator (URL) in thesource data of the session in accordance with a pre-determinedcompression coding format; and replacing the URL in the source data ofthe session with the coded URL.
 11. The system for monitoring andanalyzing website visit data of claim 7, wherein each dimensional dataincludes at least page views data and unique visitors data.
 12. Thesystem for monitoring and analyzing website visit data of claim 11,further comprising calculating a unique visitors number for eachdimensional data, wherein if an update speed is higher than apre-defined speed, the unique visitors number is calculated using a setstructure, and the user identifier of the session with respect to thedimensional data is stored in a designated memory module; and if theupdate speed is lower than the pre-defined speed, the unique visitorsnumber is calculated through determining whether a set structure ofunique visitors of a current day includes a user identifier.
 13. Anon-transitory computer readable storage medium, storing one or moreprograms for execution by one or more processors of a service processingsystem including instructions for: acquiring real-time source data ofsessions established between a client terminal and a server, comprising:scanning the acquired real-time source data of a respective session;determining whether the scanned source data is a first page of therespective session; in accordance with a determination that the scannedsource data is the first page of the respective session, acquiring userportrait data corresponding to a user identifier associated with thereal-time source data of the respective session; and updating thereal-time source data of the respective session using the user portraitdata; classifying the real-time source data into a plurality ofcategories based on the website and a session identifier; caching thecategorized real-time source data of a session corresponding to thesession identifier in the memory, the session having a session endingtime; and performing the following operations in real-time: comparingthe session ending time with a last updating time of a sum of visiteffect data associated with the website; if the session ending time islater than the last updating time, calculating visit effect data of thesession using the categorized real-time source data of the session,further including generating traffic analysis data, source analysisdata, visitor analysis data, and visitor behavior analysis dataassociated with the website; consolidating the visit effect data of thesession with the sum of visit effect data associated with the website;keeping the categorized real-time source data of the session in thememory to be combined with subsequent real-time source data of thesession before the session is completed; and if the session ending timeis no later than the last updating time, calculating completed visiteffect data of the session using a completed source data of the sessionstored in the memory, further including generating a total length of thesession, webpage visits information, webpage visiting path andassociation information, and an exit rate of the session associated withthe website; consolidating the completed visit effect data of thesession with the sum of visit effect data associated with the website;and deleting the completed source data of the session from the memory torefresh the memory; updating the sum of visit effect data associatedwith the website.
 14. The non-transitory computer readable storagemedium of claim 13, wherein the one or more programs further includeinstructions for: sorting the real-time source data of sessions inaccordance with an occurrence time of the session.
 15. Thenon-transitory computer readable storage medium of claim 13, wherein theone or more programs further include instructions for: acquiring theuser identifier for each session; and supplementing the user portraitdata into the source data of the session.
 16. The non-transitorycomputer readable storage medium of claim 13, wherein the one or moreprograms further include instructions for: coding a uniform resourcelocator (URL) in the source data of the session in accordance with apre-determined compression coding format; and replacing the URL in thesource data of the session with the coded URL.